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EDAR and DESCRIPA study groups

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Bioinformatics· 1

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Approach:Bayesian inference· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:proteomics· 1

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Alzheimer’s Disease Neuroimaging Initiative· 1 shared Amos FolariniD· 1 shared Angela HodgesiD· 1 shared Aoife KeohaneiD· 1 shared Caroline JohnstoniD· 1 shared Hamel PateliD· 1 shared Kuang Lin· 1 shared Nicola Voyle· 1 shared

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2017-2017 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Alzheimer’s Disease Neuroimaging Initiative

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1 paper

Recent·Most cited

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

Nicola Voyle, Willemijn J. Jansen, Aoife Keohane, Hamel Patel, Amos Folarin, Stephen Newhouse, Caroline Johnston, Kuang Lin, Pieter Jelle Visser, Angela Hodges, Richard Dobson, Steven John Kiddle, Alzheimer’s Disease Neuroimaging Initiative, EDAR and DESCRIPA study groups

This study uses Bayesian logistic regression to assess if genetic risk factors predict amyloid burden in non-demented individuals, finding that known factors like age and APOE are more predictive than polygenic risk scores.

bioRxiv · Aug 10, 2017 · PreprintBioinformatics